Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
# install.packages("gganimate")
# install.packages("gifski")
# install.packages("av")
# install.packages("gapminder")
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.0 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(gganimate)
library(gifski)
library(av)
library(gapminder)
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)
## tibble [1,704 x 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
We see an interesting spread with an outlier to the right. Answer the following questions, please:
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
labs(title = 'Regular x-axis')
ggplot(subset(gapminder, year == 1952 & gdpPercap<30000), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
labs(title = 'Regular x-axis, outlier removed')
ggplot(subset(gapminder, year == 1952 & gdpPercap<30000), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
labs(title = 'Log10 x-axis, outlier removed') +
scale_x_log10()
filter(gapminder, year==1952 & gdpPercap > 30000)
## # A tibble: 1 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Kuwait Asia 1952 55.6 160000 108382.
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Tasks:
# Load the scales package for removing scientific notation in legend
library(scales)
##
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
##
## discard
## The following object is masked from 'package:readr':
##
## col_factor
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_x_log10() +
scale_size_continuous(labels = comma) + # removes sci-notation
labs(x='GDP per capita', y='Life expectancy', color='Continent', size='Population size')
gapminder %>% filter(year==2007) %>% arrange(desc(gdpPercap)) %>% slice(1:5)
## # A tibble: 5 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Norway Europe 2007 80.2 4627926 49357.
## 2 Kuwait Asia 2007 77.6 2505559 47307.
## 3 Singapore Asia 2007 80.0 4553009 47143.
## 4 United States Americas 2007 78.2 301139947 42952.
## 5 Ireland Europe 2007 78.9 4109086 40676.
The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.
Also, there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
anim
…
This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
…
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages
transition_states() and transition_time() functions respectively)anim2 + labs(title='Year: {frame_time}')
ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_size_continuous(labels = comma) +
scale_x_continuous(labels = comma, trans = 'log10') +
transition_time(year) +
labs(x='GDP per capita', y='Life expectancy', color='Continent', size='Population size', title='Year: {frame_time}')
gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]How has the mean population size within each continent changed from 1952 to 2007?
# Prepare data frame for ggplot
continent_pop <- gapminder %>%
group_by(continent, year) %>%
summarise(mean_pop=mean(pop)) %>%
filter(year==1952 | year==2007)
## `summarise()` has grouped output by 'continent'. You can override using the `.groups` argument.
# Prepare a second data frame with additional info which will be added to the first data frame
continent_pop2 <- data.frame(continent=c('Africa', 'Americas', 'Asia', 'Europe', 'Oceania'), year=rep('Growth', 5), mean_pop=rep(0, 5))
# A for-loop that calculates the growth in mean population size within each continent and puts the result in the second data frame.
for (i in continent_pop2$continent){
n <- which(continent_pop2$continent == i)
year1952 <- continent_pop[continent_pop$continent==i & continent_pop$year==1952,]$mean_pop
year2007 <- continent_pop[continent_pop$continent==i & continent_pop$year==2007,]$mean_pop
continent_pop2$mean_pop[n] <- as.integer(year2007-year1952)
}
# Combine the two data frames
continent_pop$year <- as.character(continent_pop$year)
continent_pop <- rbind(continent_pop, continent_pop2)
# Factorize the year column to make it compliant with ggplot
continent_pop$year <- as.factor(continent_pop$year)
# Draw the plot
ggplot(continent_pop, aes(x=continent, y=mean_pop, fill=year)) +
geom_bar(position = 'dodge', stat = 'identity') +
scale_y_continuous(labels = comma) +
scale_fill_discrete(name=NULL) +
theme(panel.grid.major.x = element_blank()) +
labs(x='Continent', y='Mean population size', fill='Year')
From the plot we see that the mean population size has increased in all continents from 1952 to 2007, and by adding the Growth-bars we can more easily see by how much. We see that the Growth-bar for Asia is by far the tallest, but that’s not a surprise considering that population sizes increase exponentially and Asia had the greatest population size from the start in 1952. By comparing the height of the 1952-bar and Growth-bar we get a better picture of how reproductive the different populations have been. We see that in Europe, the mean population size has increased by less than 50% (the Growth-bar is not even half the height of the 1952-bar) while in Africa, the mean population size has increased by more than 100%, making it the continent with the greatest mean population size growth rate.